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Our approach: Microsoft AI principles. https:\/\/www.microsoft.com\/en-us\/ai\/our-approach-to-ai"},{"key":"e_1_3_2_1_3_1","volume-title":"International Conference on Machine Learning. 50--59","author":"Adel Tameem","year":"2018","unstructured":"Tameem Adel , Zoubin Ghahramani , and Adrian Weller . 2018 . Discovering interpretable representations for both deep generative and discriminative models . In International Conference on Machine Learning. 50--59 . Tameem Adel, Zoubin Ghahramani, and Adrian Weller. 2018. Discovering interpretable representations for both deep generative and discriminative models. In International Conference on Machine Learning. 50--59."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Sarah Adel Bargal Andrea Zunino Donghyun Kim Jianming Zhang Vittorio Murino and Stan Sclaroff. 2018. Excitation backprop for RNNs. In 'Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition'. 1440--1449. Sarah Adel Bargal Andrea Zunino Donghyun Kim Jianming Zhang Vittorio Murino and Stan Sclaroff. 2018. Excitation backprop for RNNs. In 'Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition'. 1440--1449.","DOI":"10.1109\/CVPR.2018.00156"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173860"},{"key":"e_1_3_2_1_6_1","volume-title":"Concrete problems in AI safety. arXiv preprint arXiv:1606.06565","author":"Amodei Dario","year":"2016","unstructured":"Dario Amodei , Chris Olah , Jacob Steinhardt , Paul Christiano , John Schulman , and Dan Man\u00e9 . 2016. Concrete problems in AI safety. arXiv preprint arXiv:1606.06565 ( 2016 ). Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Man\u00e9. 2016. Concrete problems in AI safety. arXiv preprint arXiv:1606.06565 (2016)."},{"key":"e_1_3_2_1_7_1","volume-title":"6th International Conference on Learning Representations (ICLR","author":"Ancona Marco","year":"2018","unstructured":"Marco Ancona , Enea Ceolini , Cengiz Oztireli , and Markus Gross . 2018 . Towards better understanding of gradient-based attribution methods for Deep Neural Networks . In 6th International Conference on Learning Representations (ICLR 2018). Marco Ancona, Enea Ceolini, Cengiz Oztireli, and Markus Gross. 2018. Towards better understanding of gradient-based attribution methods for Deep Neural Networks. In 6th International Conference on Learning Representations (ICLR 2018)."},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.)","volume":"97","author":"Ancona Marco","year":"2019","unstructured":"Marco Ancona , Cengiz Oztireli , and Markus Gross . 2019 . Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation . In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.) , Vol. 97 . PMLR, Long Beach, California, USA, 272--281. Marco Ancona, Cengiz Oztireli, and Markus Gross. 2019. Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. PMLR, Long Beach, California, USA, 272--281."},{"key":"e_1_3_2_1_9_1","first-page":"1803","article-title":"How to explain individual classification decisions","author":"Baehrens David","year":"2010","unstructured":"David Baehrens , Timon Schroeter , Stefan Harmeling , Motoaki Kawanabe , Katja Hansen , and Klaus-Robert M\u00c3\u017eller . 2010 . How to explain individual classification decisions . Journal of Machine Learning Research 11 , Jun (2010), 1803 -- 1831 . David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert M\u00c3\u017eller. 2010. How to explain individual classification decisions. Journal of Machine Learning Research 11, Jun (2010), 1803--1831.","journal-title":"Journal of Machine Learning Research 11"},{"key":"e_1_3_2_1_10_1","unstructured":"Rajiv Khanna Been Kim and Sanmi Koyejo. 2016. Examples are not Enough Learn to Criticize! Criticism for Interpretability. In Advances in Neural Information Processing Systems. Rajiv Khanna Been Kim and Sanmi Koyejo. 2016. Examples are not Enough Learn to Criticize! Criticism for Interpretability. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_11_1","volume-title":"Moura","author":"Bhatt Umang","year":"2019","unstructured":"Umang Bhatt , Pradeep Ravikumar , and Jos\u00e9 M. F . Moura . 2019 . Towards Aggregating Weighted Feature Attributions . abs\/1901.10040 (2019). Umang Bhatt, Pradeep Ravikumar, and Jos\u00e9 M. F. Moura. 2019. Towards Aggregating Weighted Feature Attributions. abs\/1901.10040 (2019)."},{"key":"e_1_3_2_1_12_1","unstructured":"Miles Brundage Shahar Avin Jack Clark Helen Toner Peter Eckersley Ben Garfinkel Allan Dafoe Paul Scharre Thomas Zeitzoff Bobby Filar etal 2018. The malicious use of artificial intelligence: Forecasting prevention and mitigation. arXiv preprint arXiv:1802.07228 (2018). Miles Brundage Shahar Avin Jack Clark Helen Toner Peter Eckersley Ben Garfinkel Allan Dafoe Paul Scharre Thomas Zeitzoff Bobby Filar et al. 2018. The malicious use of artificial intelligence: Forecasting prevention and mitigation. arXiv preprint arXiv:1802.07228 (2018)."},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.)","volume":"97","author":"Chattopadhyay Aditya","year":"2019","unstructured":"Aditya Chattopadhyay , Piyushi Manupriya , Anirban Sarkar , and Vineeth N Balasubramanian . 2019 . Neural Network Attributions: A Causal Perspective . In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.) , Vol. 97 . PMLR, Long Beach, California, USA, 981--990. Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, and Vineeth N Balasubramanian. 2019. Neural Network Attributions: A Causal Perspective. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. PMLR, Long Beach, California, USA, 981--990."},{"key":"e_1_3_2_1_14_1","volume-title":"7th International Conference on Learning Representations (ICLR 2019)","author":"Chen Jianbo","unstructured":"Jianbo Chen , Le Song , Martin J Wainwright , and Michael I Jordan . [n. d.]. L-shapley and c-shapley: Efficient model interpretation for structured data . 7th International Conference on Learning Representations (ICLR 2019) ([n. d.]). Jianbo Chen, Le Song, Martin J Wainwright, and Michael I Jordan. [n. d.]. L-shapley and c-shapley: Efficient model interpretation for structured data. 7th International Conference on Learning Representations (ICLR 2019) ([n. d.])."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1080\/00401706.1977.10489493","article-title":"Detection of influential observation in linear regression","volume":"19","author":"Cook R Dennis","year":"1977","unstructured":"R Dennis Cook . 1977 . Detection of influential observation in linear regression . Technometrics 19 , 1 (1977), 15 -- 18 . R Dennis Cook. 1977. Detection of influential observation in linear regression. Technometrics 19, 1 (1977), 15--18.","journal-title":"Technometrics"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Jeffrey De Fauw Joseph R Ledsam Bernardino Romera-Paredes Stanislav Nikolov Nenad Tomasev Sam Blackwell Harry Askham Xavier Glorot Brendan O'Donoghue Daniel Visentin etal 2018. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature medicine 24 9 (2018) 1342. Jeffrey De Fauw Joseph R Ledsam Bernardino Romera-Paredes Stanislav Nikolov Nenad Tomasev Sam Blackwell Harry Askham Xavier Glorot Brendan O'Donoghue Daniel Visentin et al. 2018. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature medicine 24 9 (2018) 1342.","DOI":"10.1038\/s41591-018-0107-6"},{"key":"e_1_3_2_1_17_1","unstructured":"Amit Dhurandhar Karthikeyan Shanmugam Ronny Luss and Peder A Olsen. 2018. Improving simple models with confidence profiles. In Advances in Neural Information Processing Systems. 10296--10306. Amit Dhurandhar Karthikeyan Shanmugam Ronny Luss and Peder A Olsen. 2018. Improving simple models with confidence profiles. In Advances in Neural Information Processing Systems. 10296--10306."},{"key":"e_1_3_2_1_18_1","volume-title":"Explanations can be manipulated and geometry is to blame. arXiv preprint arXiv:1906.07983","author":"Dombrowski Ann-Kathrin","year":"2019","unstructured":"Ann-Kathrin Dombrowski , Maximilian Alber , Christopher J Anders , Marcel Ackermann , Klaus-Robert M\u00fcller , and Pan Kessel . 2019. Explanations can be manipulated and geometry is to blame. arXiv preprint arXiv:1906.07983 ( 2019 ). Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J Anders, Marcel Ackermann, Klaus-Robert M\u00fcller, and Pan Kessel. 2019. Explanations can be manipulated and geometry is to blame. arXiv preprint arXiv:1906.07983 (2019)."},{"key":"e_1_3_2_1_19_1","unstructured":"Finale Doshi-Velez and Been Kim. 2017. Towards A Rigorous Science of Interpretable Machine Learning. (2017). Finale Doshi-Velez and Been Kim. 2017. Towards A Rigorous Science of Interpretable Machine Learning. (2017)."},{"key":"e_1_3_2_1_20_1","volume-title":"Handbook of Massive Data Sets","author":"DuMouchel William","unstructured":"William DuMouchel . 2002. Data squashing: constructing summary data sets . In Handbook of Massive Data Sets . Springer , 579--591. William DuMouchel. 2002. Data squashing: constructing summary data sets. In Handbook of Massive Data Sets. Springer, 579--591."},{"key":"e_1_3_2_1_21_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.)","volume":"97","author":"Etmann Christian","year":"2019","unstructured":"Christian Etmann , Sebastian Lunz , Peter Maass , and Carola Schoenlieb . 2019 . On the Connection Between Adversarial Robustness and Saliency Map Interpretability . In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.) , Vol. 97 . PMLR, Long Beach, California, USA , 1823--1832. Christian Etmann, Sebastian Lunz, Peter Maass, and Carola Schoenlieb. 2019. On the Connection Between Adversarial Robustness and Saliency Map Interpretability. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. PMLR, Long Beach, California, USA, 1823--1832."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.371"},{"key":"e_1_3_2_1_23_1","volume-title":"Interpretation of neural networks is fragile. AAAI","author":"Ghorbani Amirata","year":"2019","unstructured":"Amirata Ghorbani , Abubakar Abid , and James Zou . 2019. Interpretation of neural networks is fragile. AAAI ( 2019 ). Amirata Ghorbani, Abubakar Abid, and James Zou. 2019. Interpretation of neural networks is fragile. AAAI (2019)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2018.00018"},{"key":"e_1_3_2_1_25_1","volume-title":"Interpretable and Differentially Private Predictions. arXiv preprint arXiv:1906.02004","author":"Harder Frederik","year":"2019","unstructured":"Frederik Harder , Matthias Bauer , and Mijung Park . 2019. Interpretable and Differentially Private Predictions. arXiv preprint arXiv:1906.02004 ( 2019 ). Frederik Harder, Matthias Bauer, and Mijung Park. 2019. Interpretable and Differentially Private Predictions. arXiv preprint arXiv:1906.02004 (2019)."},{"key":"e_1_3_2_1_26_1","volume-title":"Deep learning in finance. arXiv preprint arXiv:1602.06561","author":"Heaton JB","year":"2016","unstructured":"JB Heaton , Nicholas G Polson , and Jan Hendrik Witte . 2016. Deep learning in finance. arXiv preprint arXiv:1602.06561 ( 2016 ). JB Heaton, Nicholas G Polson, and Jan Hendrik Witte. 2016. Deep learning in finance. arXiv preprint arXiv:1602.06561 (2016)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1986.10478354"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300830"},{"key":"e_1_3_2_1_29_1","volume-title":"Please Stop Permuting Features: An Explanation and Alternatives. arXiv preprint arXiv:1905.03151","author":"Hooker Giles","year":"2019","unstructured":"Giles Hooker and Lucas Mentch . 2019. Please Stop Permuting Features: An Explanation and Alternatives. arXiv preprint arXiv:1905.03151 ( 2019 ). Giles Hooker and Lucas Mentch. 2019. Please Stop Permuting Features: An Explanation and Alternatives. arXiv preprint arXiv:1905.03151 (2019)."},{"key":"e_1_3_2_1_30_1","unstructured":"Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran and Aleksander Madry. 2019. Adversarial Examples Are Not Bugs They Are Features. http:\/\/arxiv.org\/abs\/1905.02175 cite arxiv:1905.02175. Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran and Aleksander Madry. 2019. Adversarial Examples Are Not Bugs They Are Features. http:\/\/arxiv.org\/abs\/1905.02175 cite arxiv:1905.02175."},{"key":"e_1_3_2_1_31_1","volume-title":"Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). arXiv preprint arXiv:1711.11279","author":"Kim Been","year":"2017","unstructured":"Been Kim , Martin Wattenberg , Justin Gilmer , Carrie Cai , James Wexler , Fernanda Viegas , and Rory Sayres . 2017. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). arXiv preprint arXiv:1711.11279 ( 2017 ). Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres. 2017. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). arXiv preprint arXiv:1711.11279 (2017)."},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning-Volume 70 (ICML 2017","author":"Koh Pang Wei","year":"2017","unstructured":"Pang Wei Koh and Percy Liang . 2017 . Understanding black-box predictions via influence functions . In Proceedings of the 34th International Conference on Machine Learning-Volume 70 (ICML 2017 ). Journal of Machine Learning Research , 1885--1894. Pang Wei Koh and Percy Liang. 2017. Understanding black-box predictions via influence functions. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 (ICML 2017). Journal of Machine Learning Research, 1885--1894."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13347-017-0279-x"},{"key":"e_1_3_2_1_34_1","volume-title":"Advances in Neural Information Processing Systems 30 (NeurIPS","author":"Lundberg Scott M","year":"2017","unstructured":"Scott M Lundberg and Su-In Lee . 2017. A Unified Approach to Interpreting Model Predictions . In Advances in Neural Information Processing Systems 30 (NeurIPS 2017 ), I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc ., 4765--4774. Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems 30 (NeurIPS 2017), I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 4765--4774."},{"key":"e_1_3_2_1_35_1","volume-title":"Shu-Fang Newman, Jerry Kim, et al.","author":"Lundberg Scott M","year":"2018","unstructured":"Scott M Lundberg , Bala Nair , Monica S Vavilala , Mayumi Horibe , Michael J Eisses , Trevor Adams , David E Liston , Daniel King-Wai Low , Shu-Fang Newman, Jerry Kim, et al. 2018 . Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nature biomedical engineering 2, 10 (2018), 749. Scott M Lundberg, Bala Nair, Monica S Vavilala, Mayumi Horibe, Michael J Eisses, Trevor Adams, David E Liston, Daniel King-Wai Low, Shu-Fang Newman, Jerry Kim, et al. 2018. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nature biomedical engineering 2, 10 (2018), 749."},{"key":"e_1_3_2_1_36_1","volume-title":"Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083","author":"Madry Aleksander","year":"2017","unstructured":"Aleksander Madry , Aleksandar Makelov , Ludwig Schmidt , Dimitris Tsipras , and Adrian Vladu . 2017. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 ( 2017 ). Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)."},{"key":"e_1_3_2_1_37_1","volume-title":"Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence","author":"Miller Tim","year":"2018","unstructured":"Tim Miller . 2018. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence ( 2018 ). Tim Miller. 2018. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence (2018)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287562"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287596"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287574"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.11.008"},{"key":"e_1_3_2_1_42_1","volume-title":"Word Embedding based Edit Distance. arXiv preprint arXiv:1810.10752","author":"Niu Yilin","year":"2018","unstructured":"Yilin Niu , Chao Qiao , Hang Li , and Minlie Huang . 2018. Word Embedding based Edit Distance. arXiv preprint arXiv:1810.10752 ( 2018 ). Yilin Niu, Chao Qiao, Hang Li, and Minlie Huang. 2018. Word Embedding based Edit Distance. arXiv preprint arXiv:1810.10752 (2018)."},{"key":"e_1_3_2_1_43_1","volume-title":"Supervisory Guidance on Model Risk Management. https:\/\/www.federalreserve.gov\/supervisionreg\/srletters\/sr1107a1.pdf","author":"Federal Reserve System Board","year":"2011","unstructured":"Board of Governors of the Federal Reserve System . 2011. Supervisory Guidance on Model Risk Management. https:\/\/www.federalreserve.gov\/supervisionreg\/srletters\/sr1107a1.pdf ( 2011 ). Board of Governors of the Federal Reserve System. 2011. Supervisory Guidance on Model Risk Management. https:\/\/www.federalreserve.gov\/supervisionreg\/srletters\/sr1107a1.pdf (2011)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1080\/09672559.2018.1454637"},{"key":"e_1_3_2_1_45_1","first-page":"13","article-title":"European Union General Data Protection Regulation","author":"European Parliament and Council of European Union.","year":"2018","unstructured":"European Parliament and Council of European Union. 2018 . European Union General Data Protection Regulation , Articles 13 - 15 . http:\/\/www.privacy-regulation.eu\/en\/13.htm (2018). European Parliament and Council of European Union. 2018. European Union General Data Protection Regulation, Articles 13-15. http:\/\/www.privacy-regulation.eu\/en\/13.htm (2018).","journal-title":"Articles"},{"key":"e_1_3_2_1_46_1","volume-title":"Causality: models, reasoning and inference","author":"Pearl Judea","unstructured":"Judea Pearl . 2000. Causality: models, reasoning and inference . Vol. 29 . Springer . Judea Pearl. 2000. Causality: models, reasoning and inference. Vol. 29. Springer."},{"key":"e_1_3_2_1_47_1","volume-title":"Marco OP Sampaio, and Pedro Bizarro","author":"Pinto F\u00e1bio","year":"2019","unstructured":"F\u00e1bio Pinto , Marco OP Sampaio, and Pedro Bizarro . 2019 . Automatic Model Monitoring for Data Streams . arXiv preprint arXiv:1908.04240 (2019). F\u00e1bio Pinto, Marco OP Sampaio, and Pedro Bizarro. 2019. Automatic Model Monitoring for Data Streams. arXiv preprint arXiv:1908.04240 (2019)."},{"key":"e_1_3_2_1_48_1","volume-title":"Jennifer Wortman Vaughan, and Hanna Wallach","author":"Poursabzi-Sangdeh Forough","year":"2018","unstructured":"Forough Poursabzi-Sangdeh , Daniel G Goldstein , Jake M Hofman , Jennifer Wortman Vaughan, and Hanna Wallach . 2018 . Manipulating and measuring model interpretability. arXiv preprint arXiv:1802.07810 (2018). Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. 2018. Manipulating and measuring model interpretability. arXiv preprint arXiv:1802.07810 (2018)."},{"key":"e_1_3_2_1_49_1","volume-title":"Stakeholders in explainable AI. arXiv preprint arXiv:1810.00184","author":"Preece Alun","year":"2018","unstructured":"Alun Preece , Dan Harborne , Dave Braines , Richard Tomsett , and Supriyo Chakraborty . 2018. Stakeholders in explainable AI. arXiv preprint arXiv:1810.00184 ( 2018 ). Alun Preece, Dan Harborne, Dave Braines, Richard Tomsett, and Supriyo Chakraborty. 2018. Stakeholders in explainable AI. arXiv preprint arXiv:1810.00184 (2018)."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/371"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0048-x"},{"key":"e_1_3_2_1_53_1","first-page":"1085","article-title":"The intuitive appeal of explainable machines","volume":"87","author":"Selbst Andrew D","year":"2018","unstructured":"Andrew D Selbst and Solon Barocas . 2018 . The intuitive appeal of explainable machines . Fordham L. Rev. 87 (2018), 1085 . Andrew D Selbst and Solon Barocas. 2018. The intuitive appeal of explainable machines. Fordham L. Rev. 87 (2018), 1085.","journal-title":"Fordham L. Rev."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"crossref","unstructured":"Lloyd S Shapley. 1953. A Value for n-Person Games. In Contributions to the Theory of Games II. 307--317. Lloyd S Shapley. 1953. A Value for n-Person Games. In Contributions to the Theory of Games II. 307--317.","DOI":"10.1515\/9781400881970-018"},{"key":"e_1_3_2_1_55_1","volume-title":"CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models. arXiv preprint arXiv:1905.07857","author":"Sharma Shubham","year":"2019","unstructured":"Shubham Sharma , Jette Henderson , and Joydeep Ghosh . 2019 . CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models. arXiv preprint arXiv:1905.07857 (2019). Shubham Sharma, Jette Henderson, and Joydeep Ghosh. 2019. CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models. arXiv preprint arXiv:1905.07857 (2019)."},{"key":"e_1_3_2_1_56_1","volume-title":"Privacy Risks of Explaining Machine Learning Models. arXiv preprint arXiv:1907.00164","author":"Shokri Reza","year":"2019","unstructured":"Reza Shokri , Martin Strobel , and Yair Zick . 2019. Privacy Risks of Explaining Machine Learning Models. arXiv preprint arXiv:1907.00164 ( 2019 ). Reza Shokri, Martin Strobel, and Yair Zick. 2019. Privacy Risks of Explaining Machine Learning Models. arXiv preprint arXiv:1907.00164 (2019)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305890.3306006"},{"key":"e_1_3_2_1_58_1","volume-title":"Gkmexplain: Fast and Accurate Interpretation of Nonlinear Gapped k-mer Support Vector Machines Using Integrated Gradients. BioRxiv","author":"Shrikumar Avanti","year":"2018","unstructured":"Avanti Shrikumar , Eva Prakash , and Anshul Kundaje . 2018 . Gkmexplain: Fast and Accurate Interpretation of Nonlinear Gapped k-mer Support Vector Machines Using Integrated Gradients. BioRxiv (2018), 457606. Avanti Shrikumar, Eva Prakash, and Anshul Kundaje. 2018. Gkmexplain: Fast and Accurate Interpretation of Nonlinear Gapped k-mer Support Vector Machines Using Integrated Gradients. BioRxiv (2018), 457606."},{"key":"e_1_3_2_1_59_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.)","volume":"97","author":"Singla Sahil","year":"2019","unstructured":"Sahil Singla , Eric Wallace , Shi Feng , and Soheil Feizi . 2019 . Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation . In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.) , Vol. 97 . PMLR, Long Beach, California, USA, 5848--5856. Sahil Singla, Eric Wallace, Shi Feng, and Soheil Feizi. 2019. Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. PMLR, Long Beach, California, USA, 5848--5856."},{"key":"e_1_3_2_1_60_1","volume-title":"Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825","author":"Smilkov Daniel","year":"2017","unstructured":"Daniel Smilkov , Nikhil Thorat , Been Kim , Fernanda Vi\u00e9gas , and Martin Wattenberg . 2017. Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 ( 2017 ). Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Vi\u00e9gas, and Martin Wattenberg. 2017. Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0679-x"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305890.3306024"},{"key":"e_1_3_2_1_63_1","volume-title":"25th {USENIX} Security Symposium ({USENIX} Security 16). 601--618.","author":"Tram\u00e8r Florian","unstructured":"Florian Tram\u00e8r , Fan Zhang , Ari Juels , Michael K Reiter , and Thomas Ristenpart . 2016. Stealing machine learning models via prediction apis . In 25th {USENIX} Security Symposium ({USENIX} Security 16). 601--618. Florian Tram\u00e8r, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart. 2016. Stealing machine learning models via prediction apis. In 25th {USENIX} Security Symposium ({USENIX} Security 16). 601--618."},{"key":"e_1_3_2_1_64_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SyxAb30cY7","author":"Tsipras Dimitris","year":"2019","unstructured":"Dimitris Tsipras , Shibani Santurkar , Logan Engstrom , Alexander Turner , and Aleksander Madry . 2019 . Robustness May Be at Odds with Accuracy . In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SyxAb30cY7 Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. 2019. Robustness May Be at Odds with Accuracy. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SyxAb30cY7"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287566"},{"key":"e_1_3_2_1_66_1","first-page":"841","article-title":"Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GPDR","volume":"31","author":"Wachter Sandra","year":"2017","unstructured":"Sandra Wachter , Brent Mittelstadt , and Chris Russell . 2017 . Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GPDR . Harv. JL & Tech. 31 (2017), 841 . Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2017. Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GPDR. Harv. JL & Tech. 31 (2017), 841.","journal-title":"Harv. JL & Tech."},{"key":"e_1_3_2_1_67_1","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","author":"Weller Adrian","unstructured":"Adrian Weller . 2019. Transparency: motivations and challenges . In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning . Springer , 23--40. Adrian Weller. 2019. Transparency: motivations and challenges. In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Springer, 23--40."},{"key":"e_1_3_2_1_68_1","volume-title":"The What-If Tool: Interactive Probing of Machine Learning Models. arXiv preprint arXiv:1907.04135","author":"Wexler James","year":"2019","unstructured":"James Wexler , Mahima Pushkarna , Tolga Bolukbasi , Martin Wattenberg , Fernanda Viegas , and Jimbo Wilson . 2019. The What-If Tool: Interactive Probing of Machine Learning Models. arXiv preprint arXiv:1907.04135 ( 2019 ). James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Viegas, and Jimbo Wilson. 2019. The What-If Tool: Interactive Probing of Machine Learning Models. arXiv preprint arXiv:1907.04135 (2019)."},{"key":"e_1_3_2_1_69_1","volume-title":"David Inouye, and Pradeep Ravikumar.","author":"Yeh Chih-Kuan","year":"2019","unstructured":"Chih-Kuan Yeh , Cheng-Yu Hsieh , Arun Sai Suggala , David Inouye, and Pradeep Ravikumar. 2019 . How Sensitive are Sensitivity-Based Explanations ? arXiv preprint arXiv:1901.09392 (2019). Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David Inouye, and Pradeep Ravikumar. 2019. How Sensitive are Sensitivity-Based Explanations? arXiv preprint arXiv:1901.09392 (2019)."},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-017-1059-x"},{"key":"e_1_3_2_1_71_1","first-page":"12991","article-title":"Why Should You Trust My Explanation?","volume":"1904","author":"Zhang Yujia","year":"2019","unstructured":"Yujia Zhang , Kuangyan Song , Yiming Sun , Sarah Tan , and Madeleine Udell . 2019 . \" Why Should You Trust My Explanation? \" Understanding Uncertainty in LIME Explanations. arXiv:arXiv : 1904 . 12991 Yujia Zhang, Kuangyan Song, Yiming Sun, Sarah Tan, and Madeleine Udell. 2019. \"Why Should You Trust My Explanation?\" Understanding Uncertainty in LIME Explanations. arXiv:arXiv:1904.12991","journal-title":"Understanding Uncertainty in LIME Explanations. arXiv:arXiv"}],"event":{"name":"FAT* '20: Conference on Fairness, Accountability, and Transparency","location":"Barcelona Spain","acronym":"FAT* '20","sponsor":["ACM Association for Computing Machinery"]},"container-title":["Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3351095.3375624","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3351095.3375624","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:13:39Z","timestamp":1750202019000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3351095.3375624"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,27]]},"references-count":71,"alternative-id":["10.1145\/3351095.3375624","10.1145\/3351095"],"URL":"https:\/\/doi.org\/10.1145\/3351095.3375624","relation":{},"subject":[],"published":{"date-parts":[[2020,1,27]]},"assertion":[{"value":"2020-01-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}